{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dtwnet-a-dynamic-time-warping-network","title":"DTWNet: a Dynamic Time Warping Network","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Xingyu Cai","Tingyang Xu","Jin-Feng Yi","Junzhou Huang","Sanguthevar Rajasekaran"],"abstract":"Dynamic Time Warping (DTW) is widely used as a similarity measure in various domains. Due to its invariance against warping in the time axis, DTW provides more meaningful discrepancy measurements between two signals than other dis- tance measures. In this paper, we propose a novel component in an artificial neural network. In contrast to the previous successful usage of DTW as a loss function, the proposed framework leverages DTW to obtain a better feature extraction. For the first time, the DTW loss is theoretically analyzed, and a stochastic backpropogation scheme is proposed to improve the accuracy and efficiency of the DTW learning. We also demonstrate that the proposed framework can be used as a data analysis tool to perform data decomposition.","url_abs":"http://papers.nips.cc/paper/9338-dtwnet-a-dynamic-time-warping-network","url_pdf":"http://papers.nips.cc/paper/9338-dtwnet-a-dynamic-time-warping-network.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dtwnet-a-dynamic-time-warping-network","repo_url":"https://github.com/TideDancer/DTWNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"}],"methods":[{"method_slug":"dtw","method_name":"DTW"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}